Quantitative analysis of human brain activity based on language representations, such as the semantic categories of words, have been actively studied in the field of brain and neuroscience. Our study aims to generate natural language descriptions for human brain activation phenomena caused by visual stimulus by employing deep learning methods, which have gained interest as an effective approach to automatically describe natural language expressions for various type of multi-modal information, such as images. We employed an image-captioning system based on a deep learning framework as the basis for our method by learning the relationship between the brain activity data and the features of an intermediate expression of the deep neural network owing to lack of training brain data. We conducted three experiments and were able to generate natural language sentences which enabled us to quantitatively interpret brain activity.